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- Led end-to-end design and deployment of Java (Spring Boot) and Python (Flask/FastAPI) microservices on AWS, delivering ML-powered workflow recommendations that cut manual triage time by 38% and boosted automated resolutions by 22%. - Developed and secured REST APIs with token-based authentication, RBAC, and rate limiting, supporting enterprise-scale integrations across React-based UIs, external clients, and ServiceNow modules. - Engineered a production-ready ML pipeline using TensorFlow, PyTorch, and Scikit-learn, deployed via AWS Lambda and ECS for scalable, low-latency inference; optimized compute utilization and cut cloud costs by 25%. - Automated CI/CD pipelines using Docker, Kubernetes, and GitHub Actions, embedding PyTest suites, regression checks, and containerized builds; cut rollback incidents by 45% while accelerating delivery velocity and improving code quality. - Migrated and centralized logs, datasets, and ML artifacts into AWS S3, RDS, and CloudWatch, creating real-time dashboards and automated alerts that improved observability, compliance, and incident response by 30%.…- Led end-to-end design and deployment of Java (Spring Boot) and Python (Flask/FastAPI) microservices on AWS, delivering ML-powered workflow recommendations that cut manual triage time by 38% and boosted automated resolutions by 22%. - Developed and secured REST APIs with token-based authentication, RBAC, and rate limiting, supporting enterprise-scale integrations across React-based UIs, external clients, and ServiceNow modules. - Engineered a production-ready ML pipeline using TensorFlow, PyTorch, and Scikit-learn, deployed via AWS Lambda and ECS for scalable, low-latency inference; optimized compute utilization and cut cloud costs by 25%. - Automated CI/CD pipelines using Docker, Kubernetes, and GitHub Actions, embedding PyTest suites, regression checks, and containerized builds; cut rollback incidents by 45% while accelerating delivery velocity and improving code quality. - Migrated and centralized logs, datasets, and ML artifacts into AWS S3, RDS, and CloudWatch, creating real-time dashboards and automated alerts that improved observability, compliance, and incident response by 30%.

Karthik Pavoor

Full Stack Developer, Web Developer, Back-End Developer, +2

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- Led end-to-end design and deployment of Java (Spring Boot) and Python (Flask/FastAPI) microservices on AWS, delivering ML-powered workflow recommendations that cut manual triage time by 38% and boosted automated resolutions by 22%. - Developed and secured REST APIs with token-based authentication, RBAC, and rate limiting, supporting enterprise-scale integrations across React-based UIs, external clients, and ServiceNow modules. - Engineered a production-ready ML pipeline using TensorFlow, PyTorch, and Scikit-learn, deployed via AWS Lambda and ECS for scalable, low-latency inference; optimized compute utilization and cut cloud costs by 25%. - Automated CI/CD pipelines using Docker, Kubernetes, and GitHub Actions, embedding PyTest suites, regression checks, and containerized builds; cut rollback incidents by 45% while accelerating delivery velocity and improving code quality. - Migrated and centralized logs, datasets, and ML artifacts into AWS S3, RDS, and CloudWatch, creating real-time dashboards and automated alerts that improved observability, compliance, and incident response by 30%.…- Led end-to-end design and deployment of Java (Spring Boot) and Python (Flask/FastAPI) microservices on AWS, delivering ML-powered workflow recommendations that cut manual triage time by 38% and boosted automated resolutions by 22%. - Developed and secured REST APIs with token-based authentication, RBAC, and rate limiting, supporting enterprise-scale integrations across React-based UIs, external clients, and ServiceNow modules. - Engineered a production-ready ML pipeline using TensorFlow, PyTorch, and Scikit-learn, deployed via AWS Lambda and ECS for scalable, low-latency inference; optimized compute utilization and cut cloud costs by 25%. - Automated CI/CD pipelines using Docker, Kubernetes, and GitHub Actions, embedding PyTest suites, regression checks, and containerized builds; cut rollback incidents by 45% while accelerating delivery velocity and improving code quality. - Migrated and centralized logs, datasets, and ML artifacts into AWS S3, RDS, and CloudWatch, creating real-time dashboards and automated alerts that improved observability, compliance, and incident response by 30%.

Available to hire

Cincinnati, United States

Led end-to-end design and deployment of Java (Spring Boot) and Python (Flask/FastAPI) microservices on AWS, delivering ML-powered workflow recommendations that cut manual triage time by 38% and boosted automated resolutions by 22%.
Developed and secured REST APIs with token-based authentication, RBAC, and rate limiting, supporting enterprise-scale integrations across React-based UIs, external clients, and ServiceNow modules.
Engineered a production-ready ML pipeline using TensorFlow, PyTorch, and Scikit-learn, deployed via AWS Lambda and ECS for scalable, low-latency inference; optimized compute utilization and cut cloud costs by 25%.
Automated CI/CD pipelines using Docker, Kubernetes, and GitHub Actions, embedding PyTest suites, regression checks, and containerized builds; cut rollback incidents by 45% while accelerating delivery velocity and improving code quality.
Migrated and centralized logs, datasets, and ML artifacts into AWS S3, RDS, and CloudWatch, creating real-time dashboards and automated alerts that improved observability, compliance, and incident response by 30%.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Junior Software Development Engineer at Asha Tek LLC

January 1, 2025 - April 1, 2025

Developed a responsive internal dashboard using Angular and Flask for secure storage systems and real-time metadata tracking. Designed REST APIs with token-based authentication; optimized SQL queries via indexing and joins, reducing latency by 40% for high-volume transactions and filtered searches across user roles. Created CI/CD pipelines with GitHub Actions to automate tests, linting, and deployment, improving code quality and release stability across staging and production. Implemented UI components and backend logic; documented deployment processes; collaborated in Agile ceremonies including stand-ups, sprint planning, retrospectives, and code reviews.

Software Development Engineer at Cashapona Technologies

May 1, 2021 - July 1, 2023

Scaled a distributed React/Flask web application to 10,000+ MAUs on AWS EC2 by optimizing API response times (40% faster) and implementing WebSocket streaming for real-time image processing. Reduced AWS cloud costs by 30% via S3 tiering and EC2 auto-scaling (99.5% uptime for ML features). Led migration from jQuery to React (TypeScript) with lazy loading, code splitting, and performance engineering; improved page load speed by 35% and reduced UI bugs by 50% through modular components and strict type checking. Automated CI/CD pipelines with GitHub Actions in containerized testing environments, enabling 2x faster release cycles while maintaining 100% test coverage.